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Background: Family-centred critical care recognises the impact of a loved one's critical illness on his relatives. Open visiting is a strategy to improve family satisfaction and psychological outcomes by permitting unrestricted or less restricted access to visit their family member in the intensive care unit (ICU). However, increased family presence may result in increased workload and a risk of burnout for ICU staff. Objectives: The objective of this study was to evaluate ICU staff perceptions regarding visiting hours and family access in Australian and New Zealand ICUs. Secondary outcomes included an evaluation of current visiting policies, witnessed events in ICUs, and barriers to implementing open visiting policies. Design: A web-based survey open to all healthcare workers in Australia and New Zealand ICUs was distributed through local, state-based, and national critical care networks. Open visiting was defined as ICUs open for visiting >14 h per day. Main results: We received 1255 valid responses. Most respondents were nurses (n = 930, 74.1%) with a median critical care experience of 10 y. Most worked in open visiting ICUs (n = 749, 59.7%). Reported visiting hours varied greatly with a median of 20 h per day (interquartile range: 10–24 h). Open visiting was perceived as beneficial for the relatives, but less so for patients and staff (relatives: n = 845, 67.3%, patients: n = 561, 44.7%, staff: n = 257, 20.5%, p < 0.0001). Respondents from closed visiting units and nurses identified more risks from open visiting than other professional groups. Generally, staff preferred not to change from their current practice. Conclusion: We report that staff perceived open visiting as beneficial for relatives, but also identified risks to themselves, including increased workload, a risk of burnout, and a risk of occupational violence. Reluctance to change highlights the importance of addressing staff perceptions when implementing an open visiting policy.

Social norms play a key role in dietary behaviors. This study tested the underlying mechanism behind social norm effects in the context of the influences of social eating cues (individual vs. group eating cues) in fast food advertisements on motivational responses. Our results indicated that food ads with group eating cues elicited stronger appetitive activation resulting in greater orbicularis oculi activation and greater skin conductivity level, indicating more positive emotional feelings and more sympathetic arousal, compared to food ads with individual eating cues. Group eating cues also led to more favorable self-reported attitudes toward the advertised food products and brands. Overall, findings from this study suggest that exposure to food ads with group eating cues yield greater appetitive responses toward these types of ads and their products, likely creating a stronger degree of perceived social support. Implications are discussed.

Interplanetary coronal mass ejections (ICMEs) are one of the main drivers for space weather disturbances. In the past, different approaches have been used to automatically detect events in existing time series resulting from solar wind in situ observations. However, accurate and fast detection still remains a challenge when facing the large amount of data from different instruments. For the automatic detection of ICMEs we propose a pipeline using a method that has recently proven successful in medical image segmentation. Comparing it to an existing method, we find that while achieving similar results, our model outperforms the baseline regarding training time by a factor of approximately 20, thus making it more applicable for other datasets. The method has been tested on in situ data from the Wind spacecraft between 1997 and 2015 with a True Skill Statistic (TSS) of 0.64. Out of the 640 ICMEs, 466 were detected correctly by our algorithm, producing a total of 254 False Positives. Additionally, it produced reasonable results on datasets with fewer features and smaller training sets from Wind, STEREO-A and STEREO-B with True Skill Statistics of 0.56, 0.57 and 0.53, respectively. Our pipeline manages to find the start of an ICME with a mean absolute error (MAE) of around 2 hours and 56 minutes, and the end time with a MAE of 3 hours and 20 minutes. The relatively fast training allows straightforward tuning of hyperparameters and could therefore easily be used to detect other structures and phenomena in solar wind data, such as corotating interaction regions.

Ambient lighting has been identified as a motivationally relevant context that affects cognition, emotion, and behavior, including food consumption and choice. This study adds to this body of work utilizing optimal foraging theory, which predicts individuals will prefer foods that deliver the most energy while costing the least energy to obtain. The environmental contexts in which one will have to forage alter perceived energy expenditure. An experiment was performed in which young adults viewed food advertisements that varied in energy density level in either dim or bright contexts. Metrics assessing motivational activation and attitudes were obtained across and post exposure. Individuals exhibited greater motivational intensity and preference when exposed to more energy dense foods in light contexts, but preference dissipated in dark contexts. Less energy dense foods elicited more motivational preference in dark contexts compared to light. Self-reported attitudes and intentions were only affected by context. Thus, variations in lighting may have larger implications for food choice and overall health.

The implementation of body-worn cameras (BWC) by policing agencies has received widespread support from many individuals, including citizens and police officers. Despite their increasing prevalence, little is known about how the point-of-view (POV) of these cameras affects perceptions of viewers. In this research, we investigate how POV interacts with skin color of citizens in police use of force videos to affect perceptions of procedural justice. In an experimental study, participants watched eight police use of force videos—half recorded from BWC and half from an onlooker’s perspective—in which skin tone of the citizen varied. Results indicate that POV interacts with citizen skin tone such that, compared to the onlooker perspective, the BWC exacerbated viewer racial bias against dark skin tone citizens. Furthermore, identification with the police officer fully mediated this relationship. Results are discussed in relation to media theory and practical implications.

Interplanetary coronal mass ejections (ICMEs) are one of the main drivers for space weather disturbances. In the past,different machine learning approaches have been used to automatically detect events in existing time series resulting fromsolar wind in situ data. However, classification, early detection and ultimately forecasting still remain challenges when facingthe large amount of data from different instruments. We propose a pipeline using a Network similar to the ResUNet++ (Jha et al. (2019)), for the automatic detection of ICMEs. Comparing it to an existing method, we find that while achieving similar results, our model outperforms the baseline regarding GPU usage, training time and robustness to missing features, thus making it more usable for other datasets.The method has been tested on in situ data from WIND. Additionally, it produced reasonable results on STEREO A and STEREO B datasetswith less input parameters. The relatively fast training allows straightforward tuning of hyperparameters and could therefore easily be used to detect other structures and phenomena in solar wind data, such as corotating interaction regions.

We present the results of a search for multipoint in situ and imaging observations of interplanetary coronal mass ejections (ICMEs) with the Heliophysics System Observatory, from 2020 April to present day. This builds up on our recent publication in ApJ Letters introducing the living ICME lineup catalog available at https://helioforecast.space/lineups. We highlight a few new lineup events captured by those spacecraft from September to November 2021, when all were located within 50 degrees east of the Sun-Earth line. Multi-messenger observations of ICME flux ropes and shocks are much needed to make progress on the understanding of the global magnetic configuration of ICMEs, space weather forecasting, the magnetic connectivity of the solar wind to the Sun and the propagation of solar energetic particles.

Measurements of geomagnetically induced currents (GICs) in the Austrian power transmission grid have been carried out since 2014 at multiple locations. Following an analysis of the scales of GICs across the grid, we now look into forecasting the GICs from incoming solar wind data. Using nearby geomagnetic field measurements stretching back 26 years, we can estimate the local geoelectric field and consequently the GICs over longer time periods. We apply a machine learning method based on recurrent neural networks to this dataset combined with solar wind data as input. In this talk, we present the final method to forecast both the local geoelectric field E and the GICs in substations in the Austrian power grid, with our model results being compared to GIC measurements from recent years. We will discuss the current status of the model, outline limitations, and consider future applications.

© 2019 Background: Falls may result in significant patient harm. A recommended strategy to prevent falls is the use of a falls risk assessment tool, but these tools are often specific for older people. Evidence suggests context-specific tools are more effective. Although a rare event in the intensive care unit (ICU), patients in the ICU are at high risk of falling. The primary trigger for the current study was an increase in falls in the study ICU. Aim: The objective of this study was to develop and implement a valid and reliable ICU-specific falls risk assessment tool, with the aim of reducing falls. Methods: Retrospective incident-monitoring database audits were performed. Prospective validity and reliability testing of an ICU-specific tool (Tyndall Bailey Falls Risk Assessment Tool [TB FRAT]) and the existing method for assessing falls risk (ONTARIO Modified Stratify [Sydney Scoring] Falls Risk Screen) was conducted. Seven raters (nurse clinicians) independently performed falls risk assessment using both tools on two occasions for six patients. Results: Correlation for risk stratification categories between the two tools was moderate (r = 0.60, P < .001). Intrarater reliability (correlation) for individual rater's scores was strong (r = 0.86, P < .001). Interrater reliability for the TB FRAT was moderate to excellent (interclass correlations = 0.76 [95% confidence interval: 0.54–0.94]), and internal consistency was excellent (Cronbach's alpha, 0.97). Falls resulting in serious injury reduced from 3.35 per 1000 separations 12 months before implementing the specific ICU tool to 0.85 per 1000 in the 12 months after implementation. Conclusions: The TB FRAT provided a more reliable falls risk assessment than the existing method of assessing falls risk in this single-room occupancy ICU. This TB FRAT could be a valuable addition to quality improvement initiatives aimed at improving patient safety related to falls; however, adaptation to the local context should be considered.

In areas where Histoplasma is endemic in the environment, occupations involving activities exposing workers to soil that contains bird or bat droppings may pose a risk for histoplasmosis. Occupational exposures are frequently implicated in histoplasmosis outbreaks. In this paper, we review the literature on occupationally acquired histoplasmosis. We describe the epidemiology, occupational risk factors, and prevention measures according to the hierarchy of controls.

This study combined theory from the fields of communication, behavioral ecology, and ecological psychology to examine how relevant factors about food influence the timing and trajectory of our decision-making after exposure to food advertisements. Young healthy adult participants (

Objective To determine whether the use of child-centered nutrition phrases (CCNP) with repeated exposure (RE) improved willingness to try, liking, and consumption of healthful foods compared with RE alone. Design The researchers used a 2 × 2 × 4 fractionated within-subjects experimental design in the study: phrase condition (RE vs CCNP + RE) by time of measurement (preintervention, postintervention, and 1-month follow-up), by type of food (tomatoes, bell peppers, lentils, and quinoa). Setting Children were recruited from 2 early education centers; 89% participated. Participants Children aged 3–6 years old (n = 87) who were predominantly white (67%) and from middle-income homes and had parents with some higher education. Intervention Adult delivery of CCNP + RE weekly for 6 weeks. Main Outcome Measure(s) Willingness to try, change in liking, and change in consumption. Analysis Two-level random-effects models were used to account for repeated measurements of willingness to try, liking, and consumption nested within participants. Results Children exhibited greater consumption of CCNP foods at follow-up assessment compared with RE foods (b = –16.28, SE = 5.41, t(528) = 3.01; P = .003). Conclusions and Implications Use of CCNP combined with RE may encourage healthy eating, especially for novel foods that children may typically refuse.

Extensive research demonstrates that exposure to threatening anti-tobacco messages can lead to defensive message processing which reduces message effectiveness. However, research investigating whether this effect is moderated by the smoking status of the message viewer is lacking. In this study, participants (N = 48 smokers and N = 51 non-smokers) viewed and rated secondhand smoke anti-tobacco messages depicting both smoking cues and threat content, or messages depicting neither while heart rate, skin conductance, and facial EMG were recorded. Post viewing, self-reported emotional experience, level of counterarguing, and recognition memory were measured. In support of the LC4MP, there were no differences between smokers and non-smokers' responses for non-threatening messages absent in smoking cues. However, messages that contained both smoking cues and threat content were defensively processed by smokers - but not non-smokers - as indicated by significantly faster heart rate, lower recognition memory and higher self-reported negativity, arousal, and counterarguments. The theoretical and practical implications are discussed.

The bulk of mass media and substance cue research has examined the processes and effects of substance cue images in isolation under controlled experimental conditions. However, little attention has been devoted to delineating the specific outcomes associated with simultaneous polysubstance depictions. This study experimentally examined whether alcohol, marijuana, and junk food mediated cues influence craving independently and whether craving for these substances is co-facilitated by simultaneous polysubstance cue depictions. At a laboratory session, participants reported their craving to substance cues in isolation and when paired with one another. The results from this study support the incentive sensitization model and polysubstance literature indicating that alcohol and marijuana cues facilitate craving for both alcohol and marijuana above and beyond when alcohol and marijuana cues are depicted in isolation for high and low users of each substance. Junk food craving was not facilitated by alcohol and marijuana cues. Theoretically, this study supports the notion that simultaneous and repeated use of multiple substances results in greater hypersensitivity and craving to mediated polysubstance cues than the cues in isolation. Practically, this study provides a word of caution for researchers examining craving responses to mediated substance cue messages and recommendations for health professionals and message designers.

Occupational exposure to diacetyl, a butter flavor chemical, can result in obliterative bronchiolitis. Obliterative bronchiolitis is characterized by exertional dyspnea, fixed airflow obstruction, and histopathologic constrictive bronchiolitis, with bronchiolar wall fibrosis leading to luminal narrowing and obliteration. We describe a case of advanced lung disease with histopathology distinct from obliterative bronchiolitis in a 37-year-old male coffee worker following prolonged exposure to high levels of diacetyl and the related compound 2,3-pentanedione, who had no other medical, avocational, or occupational history that could account for his illness. He began working at a coffee facility in the flavoring room and grinding area in 2009. Four years later he moved to the packaging area but continued to flavor and grind coffee at least 1 full day per week. He reported chest tightness and mucous membrane irritation when working in the flavoring room and grinding area in 2010. Beginning in 2014, he developed dyspnea, intermittent cough, and a reduced sense of smell without a work-related pattern. In 2016, spirometry revealed a moderate mixed pattern that did not improve with bronchodilator. Thoracoscopic lung biopsy results demonstrated focal mild cellular bronchiolitis and pleuritis, and focal peribronchiolar giant cells/granulomas, but no evidence of constrictive bronchiolitis. Full-shift personal air-samples collected in the flavoring and grinding areas during 2016 measured diacetyl concentrations up to 84-fold higher than the recommended exposure limit. Medical evaluations indicate this worker developed work-related, airway-centric lung disease, most likely attributable to inhalational exposure to flavorings, with biopsy findings not usual for obliterative bronchiolitis. Clinicians should be aware that lung pathology could vary considerably in workers with suspected flavoring-related lung disease.

In this study we present a method for forecasting the ambient solar wind at L1 from coronal magnetic models. Ambient solar wind flows in interplanetary space determine how solar storms evolve through the heliosphere before reaching Earth, and accurately modelling and forecasting the ambient solar wind flow is therefore imperative to space weather awareness. We describe a novel machine learning approach in which solutions from models of the solar corona based on 12 different ADAPT magnetic maps are used to output the solar wind conditions some days later at the Earth. A feature analysis is carried out to determine which input variables are most important. The results of the forecasting model are compared to observations and existing models for one whole solar cycle in a comprehensive validation analysis. We find that the new model outperforms existing models and 27-day persistence in almost all metrics. The final model discussed here represents an extremely fast, well-validated and open-source approach to the forecasting of ambient solar wind at Earth, and is specifically well-suited for ensemble modelling or for application with other coronal models.

We present initial results for a triple-point analysis for the in situ magnetic field measurements of a CME observed at three independent locations. On the 19th of April 2020, Solar Orbiter observed a CME in situ at a radial distance of around 0.8 au. This CME was subsequently also detected by the Wind and Bepi Colombo satellites closer to Earth. This triple in situ measurement of a CME provides us the unique opportunity to test the consistency of the measurements with our own 3D Coronal Rope Ejection (3DCORE) model. A triple measurement allows for up to seven different data combinations to be analyzed (three single-point, three dual-point, and one single triple-point combination) which gives us information on how our analysis pipeline responds to multi-point measurements and how the results change with measurements at differing radial and longitudinal distances. The goal of this study is to test whether all three in situ measurements can still be described by a slightly bent flux rope geometry and how adding additional measurements can improve the accuracy of inferred model parameters.

We present the first results of our newly developed CME arrival prediction model, which allows the CME front to deform and adapt to the changing solar wind conditions. Our model is based on ELEvoHI and makes use of the WSA/HUX (Wang-Sheeley-Arge/Heliospheric Upwind eXtrapolation) model combination, which computes large-scale ambient solar wind conditions in the interplanetary space. With an estimate of the solar wind speed and density, we are able to account for the drag exerted on different parts of the CME front. Initially, our model relies on heliospheric imager observations to confine an elliptical CME front and to obtain an initial speed and drag parameter for the CME. After a certain distance, each point of the CME front is propagating based on the conditions in the heliosphere. In this case study, we compare our results to previous arrival time predictions using ELEvoHI with a rigid CME front. We find that the actual arrival time at Earth and the arrival time predicted by the new model are in very good agreement.

In the last years, many kinds of CME models, based on a drag-based evolution through interplanetary space, have been developed and are now widely used by the community. The unbeatable advantage of those methods is that they are computationally cheap and are therefore suitable to be used as ensemble models. Additionally, their prediction accuracy is absolutely comparable to more sophisticated models.The ELlipse Evolution model based on heliospheric imager (HI) observations (ELEvoHI) assumes an elliptic frontal shape within the ecliptic plane and allows the CME to adjust to the ambient solar wind speed, i.e. it is drag-based. ELEvoHI is used as an ensemble simulation by varying the CME frontal shape within given boundary values. The results include a frequency distribution of predicted arrival time and arrival speed and an estimation of the arrival probability.In this study, we investigate the possibility of not only varying the parameters related to the CME's ecliptic extent but also the ambient solar wind speed for each CME ensemle member. Although we have used a range of +/-100 km/s for possible values of the solar wind speed in the past, only the best candidate was in the end used to contribute to the prediction. We present the results of this approach by applying it to a CME propagating in a highly structured solar wind and compare the frequency distribution of the arrival time and speed predictions to those of the usual ELEvoHI approach.

We show in situ observations of ICMEs during the first year of Solar Orbiter observations based on magnetic field data from the MAG instrument in conjunction with in situ and imaging observations from the Heliospheric System Observatory. The in situ magnetic field data from four other currently active spacecraft - Parker Solar Probe, BepiColombo, STEREO-Ahead and Wind - are also searched for ICME signatures, and all clear ICME events that could be identified by classic signatures such as elevated and rotating magnetic fields of sufficiently long durations are included in a living online catalog. Furthermore, we provide a visualization of the in situ magnetic field data alongside spacecraft positions and propagating CME fronts, which are based on modeling of STEREO-A heliospheric imager data. This allows us to identify ICME events that could be unambiguously followed from their inception on the Sun to their impact at the aforementioned spacecraft, and highlights sought-after lineup events, in which the same ICME is observed at multiple points in space, such as the well-studied 2020 April 15-20 ICME. We discuss the ICME rate observed so far, and provide an outlook on the expected ICME rate in solar cycle 25 based on different forecasts for the cycle amplitude (see Möstl et al. 2020, https://doi.org/10.3847/1538-4357/abb9a1).

Objective The objective of this study was to describe family visitation policies, facilities, and support in Australia and New Zealand ( ANZ ) intensive care units (ICUs). Methods A survey was distributed to all Australian and New Zealand ICUs reporting to the Australian and New Zealand Intensive Care Society Centre for Outcomes and Resources Evaluation Critical Care Resources (CCR) Registry in 2018. Data were obtained from the survey and from data reported to the CCR Registry. For this study, open visiting (OV) was defined as allowing visitors for more than 14 h per day. Setting and participants This study included all Australian and New Zealand ICUs reporting to CCR in 2018. Main outcome measures The main outcome measures were family access to the ICU and visiting hours, characteristics of the ICU waiting area, and information provided to and collected from the relatives. Findings Fifty-six percent (95/170) of ICUs contributing to CCR responded, representing 44% of ANZ ICUs and a range of rural, metropolitan, tertiary, and private ICUs. Visiting hours ranged from 1.5 to 24 h per day, with 68 (72%) respondent ICUs reporting an OV policy, of which 64 (67%) ICUs were open to visitors 24 h a day. A waiting room was part of the ICU for 77 (81%) respondent ICUs, 74 (78%) reported a separate dedicated room for family meetings, and 83 (87%) reported available social worker services. Most ICUs reported facilities for sleeping within or near the hospital. An information booklet was provided by 64 (67%) ICUs. Only six (6%) ICUs required personal protective equipment for all visitors, and 76 (80%) required personal protective equipment for patients with airborne precautions. Conclusions In 2018, the majority of ANZ ICUs reported liberal visiting policies, with substantial facilities and family support.

In this study, we evaluate a coronal mass ejection (CME) arrival prediction tool that utilizes the wide‐angle observations made by STEREO's heliospheric imagers (HI). The unsurpassable advantage of these imagers is the possibility to observe the evolution and propagation of a CME from close to the Sun out to 1 AU and beyond. We believe that by exploiting this capability, instead of relying on coronagraph observations only, it is possible to improve today's CME arrival time predictions. The ELlipse Evolution model based on HI observations (ELEvoHI) assumes that the CME frontal shape within the ecliptic plane is an ellipse and allows the CME to adjust to the ambient solar wind speed; that is, it is drag based. ELEvoHI is used to perform ensemble simulations by varying the CME frontal shape within given boundary conditions that are consistent with the observations made by HI. In this work, we evaluate different setups of the model by performing hindcasts for 15 well‐defined isolated CMEs that occurred when STEREO was near L4/5, between the end of 2008 and the beginning of 2011. In this way, we find a mean absolute error of between 6.2 ± 7.9 and 9.9 ± 13 hr depending on the model setup used. ELEvoHI is specified for using data from future space weather missions carrying HIs located at L5 or L1. It can also be used with near‐real‐time STEREO‐A HI beacon data to provide CME arrival predictions during the next ∼7 years when STEREO‐A is observing the Sun‐Earth space., Key Points CME prediction tool ELEvoHI is ready to be used in real time, based on STEREO‐A/HI beacon dataDifferent model setups and inputs lead to large differences of the prediction accuraciesAccurate modeling of the ambient solar wind is of particular importance to improve CME predictions

We report the result of the first search for multipoint in situ and imaging observations of interplanetary coronal mass ejections (ICMEs) starting with the first Solar Orbiter (SolO) data in 2020 April - 2021 April. A data exploration analysis is performed including visualizations of the magnetic field and plasma observations made by the five spacecraft SolO, BepiColombo, Parker Solar Probe (PSP), Wind and STEREO-A, in connection with coronagraph and heliospheric imaging observations from STEREO-A/SECCHI and SOHO/LASCO. We identify ICME events that could be unambiguously followed with the STEREO-A heliospheric imagers during their interplanetary propagation to their impact at the aforementioned spacecraft, and look for events where the same ICME is seen in situ by widely separated spacecraft. We highlight two events: (1) a small streamer blowout CME on 2020 June 23 observed with a triple lineup by PSP, BepiColombo and Wind, guided by imaging with STEREO-A, and (2) the first fast CME of solar cycle 25 ($ \approx 1600$ km s$^{-1}$) on 2020 November 29 observed in situ by PSP and STEREO-A. These results are useful for modeling the magnetic structure of ICMEs and the interplanetary evolution and global shape of their flux ropes and shocks, and for studying the propagation of solar energetic particles. The combined data from these missions are already turning out to be a treasure trove for space weather research and are expected to become even more valuable with an increasing number of ICME events expected during the rise and maximum of solar cycle 25., Comment: in press at ApJ Letters (submitted 2021 September 15, revised 2021 November 24, accepted 2021 December 9). 11 pages, 3 figures, 1 table

During its 2011-2015 lifetime the MESSENGER spacecraft completed more than 4000 orbits around Mercury, producing vast amounts of information regarding the planetary magnetic field and magnetospheric processes. During each orbit the spacecraft left and re-entered the Hermean magnetosphere, giving us information about more than 8000 crossings of the bow shock and the magnetopause of Mercury's magnetosphere. The information obtained from the magnetometer data offers the possibility to study in depth the structures of the bow shock and magnetopause current sheets and their shapes. In this work, we take a step in this direction by automatically detecting the crossings of bow-shock and magnetopause. To this end, we propose a five-class problem and train a Convolutional Neural Network based classifier using the magnetometer data. Our key experimental results indicate that an average precision and recall of at least 87% and 96% can be achieved on the bow hock and magnetopause crossings by using only a small subset of the data. We also model the average three-dimensional shape of these boundaries depending on the external interplanetary magnetic field . Furthermore, we attempt to clarify the dependence of the two boundary locations on the heliocentric distance of Mercury and on the solar activity cycle phase. This work may be of particular interest for future Mercury research related to the BepiColombo spacecraft mission, which will enter Mercury’s orbit around December 2025.

Roasted coffee and many coffee flavorings emit volatile organic compounds (VOCs) including diacetyl and 2,3-pentanedione. Exposures to VOCs during roasting, packaging, grinding, and flavoring coffee can negatively impact the respiratory health of workers. Inhalational exposures to diacetyl and 2,3-pentanedione can cause obliterative bronchiolitis. This study summarizes exposures to and emissions of VOCs in 17 coffee roasting and packaging facilities that included 10 cafés. We collected 415 personal and 760 area full-shift, and 606 personal task-based air samples for diacetyl, 2,3-pentanedione, 2,3-hexanedione, and acetoin using silica gel tubes. We also collected 296 instantaneous activity and 312 instantaneous source air measurements for 18 VOCs using evacuated canisters. The highest personal full-shift exposure in part per billion (ppb) to diacetyl [geometric mean (GM) 21 ppb; 95th percentile (P95) 79 ppb] and 2,3-pentanedione (GM 15 ppb; P95 52 ppb) were measured for production workers in flavored coffee production areas. These workers also had the highest percentage of measurements above the NIOSH Recommended Exposure Limit (REL) for diacetyl (95%) and 2,3-pentanedione (77%). Personal exposures to diacetyl (GM 0.9 ppb; P95 6.0 ppb) and 2,3-pentanedione (GM 0.7 ppb; P95 4.4 ppb) were the lowest for non-production workers of facilities that did not flavor coffee. Job groups with the highest personal full-shift exposures to diacetyl and 2,3-pentanedione were flavoring workers (GM 34 and 38 ppb), packaging workers (GM 27 and 19 ppb) and grinder operator (GM 26 and 22 ppb), respectively, in flavored coffee facilities, and packaging workers (GM 8.0 and 4.4 ppb) and production workers (GM 6.3 and 4.6 ppb) in non-flavored coffee facilities. Baristas in cafés had mean full-shift exposures below the RELs (GM 4.1 ppb diacetyl; GM 4.6 ppb 2,3-pentanedione). The tasks, activities, and sources associated with flavoring in flavored coffee facilities and grinding in non-flavored coffee facilities, had some of the highest GM and P95 estimates for both diacetyl and 2,3-pentanedione. Controlling emissions at grinding machines and flavoring areas and isolating higher exposure areas (e.g., flavoring, grinding, and packaging areas) from the main production space and from administrative or non-production spaces is essential for maintaining exposure control.

Objective The aim of this study was to compare spirometry results in microwave popcorn and flavoring manufacturing workers. Methods We used NIOSH data on current and former microwave popcorn workers (MPWs) and surveillance data on flavoring manufacturing workers (FMWs). Results Former MPW had higher prevalence of mixed and high severity abnormalities, some had excessive lung function drops. Current MPW had lowest occurrence of excessive lung function drops. FMW with excessive drops and spirometric abnormalities at last test had developed a restrictive pattern. Spirometric abnormalities and excessive drops were associated with work-related factors. Conclusion There was evidence of a healthy worker survivor effect in MPW. Importantly, removal from exposure did not always stabilize lung function decline indicating a need for continued monitoring. The development of a restrictive pattern should raise the level of suspicion for possible work-related disease in flavoring-exposed workers.

Coronal mass ejections (CMEs) may induce strong geomagnetic storms which have a significant impact on satellites in orbit as well as electrical devices on Earth’s surface. If we want to be able to mitigate the potentially devastating consequences which strong CMEs might have on Earth, developing models which accurately predict their arrival time is an integral step. The Ellipse Evolution model based on Heliospheric Imager observations (ELEvoHl) predicts the arrival of coronal mass ejections using data from STEREO’s HI instruments. HI data is available as high-resolution science data, which is downlinked every few days and low-resolution beacon data, which is downlinked in near real-time. Therefore, to allow for real time predictions of CME arrivals, beacon data must be used. We study different data reduction procedures to improve the quality of the measurements and compile the resulting images into time-elongation plots (J-plots). We track the leading edge of each selected CME event by hand, resulting in a series of time-elongation points which function as input for the ELEvoHI model. We compare the resulting predictions to those obtained using science data in terms of accuracy and errors of the predicted arrival time and speed.

Forecasting the arrival time and speed of CMEs is of high importance. However, uncertainties in the forecasts are high. We present the results of post-event prediction of CME arrivals using ELEvoHI (ELlipse Evolution model based on Heliospheric Imager observations) ensemble modeling. The model uses time-elongation profiles provided by HI (Heliospheric Imager) onboard STEREO (Solar TErrestrial RElations Observatory) and assumes an elliptical shape of the CME front. The drag force exerted by the ambient solar wind is an essential factor influencing the dynamic evolution of CMEs in the heliosphere. To account for this effect, ELEvoHI utilizes the modeled ambient solar wind provided by the Wang-Sheeley-Arge model. We carefully select 12 CMEs between February 2010 and July 2012, which show clear signatures in STEREO-A and STEREO-B HI images, have a corresponding in-situ signature, and propagate close to the ecliptic plane. As input to ELEvoHI, we make use of STEREO-A and STEREO-B time-elongation profiles for each CME and compare the predicted arrival times and speeds based on both vantage points with each other. We present our model results and discuss possible reasons for the differences in the arrival times of up to 15 hours.

Forecasting of coronal mass ejection magnetic flux rope fields at L1 is a long-standing challenge and one of the major problems in space weather forecasting. We attempt to make progress by using two approaches: 1) machine learning approaches (e.g., linear regression, lars lasso, RANSAC, or random forest), and 2) analogue ensemble methods. For our study, we take events observed at the Wind, Stereo-A and Stereo-B satellites from the ICME list created within the EU-project HELCATS. We analyse different scores (e.g., RMSE, or the skill of the model) of the presented methods. Further, we investigate how well the flux rope field can be anticipated when the first few hours of the flux rope have already been observed at L1.

During the last years, we focused on developing a prediction tool that utilizes the wide-angle observations of STEREO's heliospheric imagers. The unsurpassable advantage of these imagers is the possibility to observe the evolution and propagation of a coronal mass ejection (CME) from close to the Sun up to 1 AU and beyond. We believe that using this advantage instead of relying on coronagraph observations that are limited to observe only 14% of the Sun-Earth line, it is possible to improve today's CME arrival time predictions.The ELlipse Evolution model based on HI observations (ELEvoHI) assumes an elliptic frontal shape within the ecliptic plane and allows the CME to adjust to the ambient solar wind speed, i.e. it is drag-based. ELEvoHI is used as an ensemble simulation by varying the CME frontal shape within given boundary values. The results include a frequency distrubution of predicted arrival time and arrival speed and an estimation of the arrival probability. ELEvoHI can be operated using several kinds of inputs. In this study we investigate 15 well-defined single CMEs when STEREO was around L4/5 between the end of 2009 and the beginning of 2011. Three different sources of input propagation directions (and shapes) are used together with three different sources of ambient solar wind speed and two different ways of defining the most appropriate fit to the HI data. The combination of these different approaches and inputs leads to 18 different model set-ups used to predict each of the 15 events in our list leading to 270 ELEvoHI ensemble predictions and all in all to almost 60000 runs. To identify the most suitable and most accurate model set-up to run ELEvoHI, we compare the predictions to the actual in situ arrival of the CMEs.This model is specified for using data from future space weather missions carrying HIs located at L5 or L1 and can also directly be used together with STEREO-A near real-time HI beacon data to provide real-time CME arrival predictions during the next 7 years when STEREO-A is observing the Sun-Earth space.

We introduce Helio4cast, an open source python package to provide real time solar wind predictions at the Sun-Earth L1 point, and to directly couple them to forecasts of the aurora oval, geomagnetically induced currents and further geomagnetic indices. We present its current status, using a combination of our PREDSTORM solar wind forecast and the real time modeling of the aurora with the OVATION model. The solar wind prediction is driven by data from either STEREO-A, a recurrence model, an empirical background solar wind model or a future L5 mission. For coronal mass ejections (CMEs), we plan to use our semi-empirical 3DCORE model to produce in situ magnetic flux rope signatures constrained by real-time solar observations, or a machine learning approach based on many previous observations of in situ CMEs. We are particularly interested in how the errors in the solar wind prediction propagate to ground-based observations. Challenges and future plans of the real-time implementation are discussed.

STEREO-B and STEREO-A are both important proxies for potential solar wind monitors at the Sun-Earth L5 point. In this study, measurements from STEREO-B are used to determine how well the Dst index in particular can be predicted using data measured near the L5 point. This is useful for determining the geoeffectivity of storms resulting from high-speed solar wind streams. Observed solar wind speeds are first mapped to the near-Earth environment as if they had been measured at L1, and the Dst is predicted from the data using a solar wind-to-Dst model. We find that Dst predicted from L5 data performs better than a recurrence model assuming the solar wind conditions repeat every 27 days, although not as well as when predicted from L1 data. The newly developed approach is currently implemented in the PREDSTORM software package to provide a real-time Dst forecast using STEREO-A data.

We evaluate a coronal mass ejection (CME) arrival prediction tool that utilizes the wide-angle observations made by STEREO's heliospheric imagers (HI), which observe the evolution and propagation of CMEs through the inner heliosphere. By exploiting this capability we aim to improve today's CME arrival time and speed predictions. The drag-based ELlipse Evolution model based on HI observations (ELEvoHI) assumes an elliptical CME frontal shape within the ecliptic plane. ELEvoHI is used to perform ensemble simulations by varying the CME frontal shape within given boundary conditions that are consistent with the observations made by HI. In this work, we evaluate different set-ups of the model by performing hindcasts for 15 well-defined isolated CMEs that occurred when STEREO was near L4/5, between the end of 2008 and the beginning of 2011. In this way, we find a mean absolute error of between 6.2+/-7.9 h and 9.9+/-13 h depending on the model set-up used. ELEvoHI is specified for using data from future space weather missions carrying HIs located at L5 and/or L1. It can also be used with near real-time STEREO-A HI beacon data to provide CME arrival predictions during the next ~7 years when STEREO-A is observing the Sun-Earth space.

Introduction: Respiratory hazards in the coffee roasting and packaging industry can include asthmagens such as green coffee bean and other dust and alpha-diketones such as diacetyl and 2,3-pentanedione that can occur naturally from roasting coffee or artificially from addition of flavoring to coffee. We sought to describe the burden of respiratory abnormalities among workers at 17 coffee roasting and packaging facilities. Methods: We completed medical surveys at 17 coffee roasting and packaging facilities that included interviewer-administered questionnaires and pulmonary function testing. We summarized work-related symptoms, diagnoses, and spirometry testing results among all participants. We compared health outcomes between participants who worked near flavoring and who did not. Results: Participants most commonly reported nose and eye symptoms, and wheeze, with a work-related pattern for some. Symptoms and pulmonary function tests were consistent with work-related asthma in some participants. About 5% of workers had abnormal spirometry and most improved after bronchodilator. Health outcomes were similar between employees who worked near flavoring and who did not, except employees who worked near flavoring reported more chronic bronchitis and ever receiving a diagnosis of asthma than those who did not work near flavoring. Conclusion: The symptoms and patterns likely represent overlapping health effects of different respiratory hazards, including green coffee bean and other dust that can contribute to work-related asthma, and diacetyl and 2,3-pentanedione that can contribute to obliterative bronchiolitis. Healthcare providers and occupational health and safety practitioners should be aware that workers at coffee roasting and packaging facilities are potentially at risk for occupational lung diseases.

We present a major update to the 3D coronal rope ejection (3DCORE) technique for modeling coronal mass ejection flux ropes in conjunction with an Approximate Bayesian Computation (ABC) algorithm that is used for fitting the model to in situ magnetic field measurements. The model assumes an empirically motivated torus-like flux rope structure that expands self-similarly within the heliosphere, is influenced by a simplified interaction with the solar wind environment, and carries along an embedded analytical magnetic field. The improved 3DCORE implementation allows us to generate extremely large ensemble simulations which we then use to find global best-fit model parameters using an ABC sequential Monte Carlo (SMC) algorithm. The usage of this algorithm, under some basic assumptions on the uncertainty of the magnetic field measurements, allows us to furthermore generate estimates on the uncertainty of model parameters using only a single in situ observation. We apply our model to synthetically generated measurements to prove the validity of our implementation for the fitting procedure. We also present a brief analysis, within the scope of our model, of an event captured by Parker Solar Probe (PSP) shortly after its first fly-by of the Sun on 2018 November 12 at 0.25 AU. The presented toolset is also easily extendable to the analysis of events captured by multiple spacecraft and will therefore facilitate future multi-point studies., Comment: 18 pages, 11 figures, in press at ApJS, accepted 10 November 2020